# OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Head

arXiv: https://arxiv.org/abs/2010.11698 · HTML: https://mlsanity.com/paper/2010.11698/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Noise transfer for unsupervised domain adaptation of retinal OCT images](https://mlsanity.com/paper/2209.08097/) (2022)
- [Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning](https://mlsanity.com/paper/2301.00504/) (2023)
- [Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal](https://mlsanity.com/paper/2407.05087/) (2024)
- [Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net](https://mlsanity.com/paper/2407.13090/) (2024)

## Abstract (arXiv)

Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 ± 0.133 to 0.142 ± 0.102, 0.449 ± 0.116 to 0.0904 ± 0.0769, 0.381 ± 0.100 to 0.0590 ± 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images.